OpenRaiser/Intern-Atlas huggingface dataset:
Fault Estimation Filter Design with Guaranteed Stability Using Markov Parameters
Venue: arXiv cs.SY 2017
Year: 2017

For additive actuator and sensor faults, we propose a systematic method to design a state-space fault estimation filter directly from Markov parameters identified from fault-free data. We address this problem by parameterizing a system-inversion-based fault estimation filter with the identified Markov parameters. Even without building an explicit state-space plant model, our novel approach still allows the filter gain design for stabilization and suboptimal $\mathcal{H}_2$ performance. This design freedom cannot be achieved by other existing data-driven fault estimation filter designs so far. Another benefit of our proposed design is the convenience of determining the state order: a higher state order of the filter leads to better estimation performance, at the cost of heavier computational burden. In contrast, order determination is cumbersome when using an identified state-space plant model for the filter design, because of the complicated propagation of the model mismatch into the fault estimation errors. Simulations using an unstable aircraft system illustrate the effectiveness of the proposed new method.
Convolutional Sparse Coding with Overlapping Group Norms
Venue: arXiv cs.CV 2017
Year: 2017

The most widely used form of convolutional sparse coding uses an $\ell_1$ regularization term. While this approach has been successful in a variety of applications, a limitation of the $\ell_1$ penalty is that it is homogeneous across the spatial and filter index dimensions of the sparse representation array, so that sparsity cannot be separately controlled across these dimensions. The present paper considers the consequences of replacing the $\ell_1$ penalty with a mixed group norm, motivated by recent theoretical results for convolutional sparse representations. Algorithms are developed for solving the resulting problems, which are quite challenging, and the impact on the performance of the denoising problem is evaluated. The mixed group norms are found to perform very poorly in this application. While their performance is greatly improved by introducing a weighting strategy, such a strategy also improves the performance obtained from the much simpler and computationally cheaper $\ell_1$ norm.
Planar L-Drawings of Directed Graphs
Venue: arXiv cs.DS 2017
Year: 2017

We study planar drawings of directed graphs in the L-drawing standard. We provide necessary conditions for the existence of these drawings and show that testing for the existence of a planar L-drawing is an NP-complete problem. Motivated by this result, we focus on upward-planar L-drawings. We show that directed st-graphs admitting an upward- (resp. upward-rightward-) planar L-drawing are exactly those admitting a bitonic (resp. monotonically increasing) st-ordering. We give a linear-time algorithm that computes a bitonic (resp. monotonically increasing) st-ordering of a planar st-graph or reports that there exists none.
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Venue: arXiv cs.NA 2017
Year: 2017

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.
A Greedy Part Assignment Algorithm for Real-time Multi-person 2D Pose Estimation
Venue: arXiv cs.CV 2017
Year: 2017

Human pose-estimation in a multi-person image involves detection of various body parts and grouping them into individual person clusters. While the former task is challenging due to mutual occlusions, the combinatorial complexity of the latter task is very high. We propose a greedy part assignment algorithm that exploits the inherent structure of the human body to achieve a lower complexity, compared to any of the prior published works. This is accomplished by (i) reducing the number of part-candidates using the estimated number of people in the image, (ii) doing a greedy sequential assignment of part-classes, following the kinematic chain from head to ankle (iii) doing a greedy assignment of parts in each part-class set, to person-clusters (iv) limiting the candidate person clusters to the most proximal clusters using human anthropometric data and (v) using only a specific subset of pre-assigned parts for establishing pairwise structural constraints. We show that, these steps result in a sparse body parts relationship graph and reduces the complexity. We also propose methods for improving the accuracy of pose-estimation by (i) spawning person-clusters from any unassigned significant body part and (ii) suppressing hallucinated parts. On the MPII multi-person pose database, pose-estimation using the proposed method takes only 0.14 seconds per image. We show that, our proposed algorithm, by using a large spatial and structural context, achieves the state-of-the-art accuracy on both MPII and WAF multi-person pose datasets, demonstrating the robustness of our approach.
Reliable Granular References to Changing Linked Data
Venue: arXiv cs.DL 2017
Year: 2017

Nanopublications are a concept to represent Linked Data in a granular and provenance-aware manner, which has been successfully applied to a number of scientific datasets. We demonstrated in previous work how we can establish reliable and verifiable identifiers for nanopublications and sets thereof. Further adoption of these techniques, however, was probably hindered by the fact that nanopublications can lead to an explosion in the number of triples due to auxiliary information about the structure of each nanopublication and repetitive provenance and metadata. We demonstrate here that this significant overhead disappears once we take the version history of nanopublication datasets into account, calculate incremental updates, and allow users to deal with the specific subsets they need. We show that the total size and overhead of evolving scientific datasets is reduced, and typical subsets that researchers use for their analyses can be referenced and retrieved efficiently with optimized precision, persistence, and reliability.
Insensitivity of the mean-field Limit of Loss Systems Under Power-of-d Routing
Venue: arXiv cs.PF 2017
Year: 2017

In this paper, we study large multi-server loss models under power-of-$d$ routing scheme when service time distributions are general with finite mean. Previous works have addressed the exponential service time case when the number of servers goes to infinity giving rise to a mean field model. The fixed point of limiting mean field equations (MFE) was shown to be insensitive to the service time distribution through simulation. Showing insensitivity to general service time distributions has remained an open problem. Obtaining the MFE in this case poses a challenge due to the resulting Markov description of the system being in positive orthant as opposed to a finite chain in the exponential case. In this paper, we first obtain the MFE and then show that the MFE has a unique fixed point that coincides with the fixed point in the exponential case thus establishing insensitivity. The approach is via a measure-valued Markov process representation and the martingale problem to establish the mean-field limit. The techniques can be applied to other queueing models.
Proposal for a fully decentralized blockchain and proof-of-work algorithm for solving NP-complete problems
Venue: arXiv cs.DC 2017
Year: 2017

We propose a proof-of-work algorithm that rewards blockchain miners for using computational resources to solve NP-complete puzzles. The resulting blockchain will publicly store and improve solutions to problems with real world applications while maintaining a secure and fully functional transaction ledger.
A Scalable and Statistically Robust Beam Alignment Technique for mm-Wave Systems
Venue: arXiv cs.IT 2017
Year: 2017

Millimeter-Wave (mm-Wave) frequency bands provide an opportunity for much wider channel bandwidth compared with the traditional sub-6 GHz band. Communication at mm-Waves is, however, quite challenging due to the severe propagation path loss. To cope with this problem, directional beamforming both at the Base Station (BS) side and at the user side is necessary in order to establish a strong path conveying enough signal power. Finding such beamforming directions is referred to as the Beam Alignment (BA) and is known to be a challenging problem. This paper presents a new scheme for efficient BA, based on the estimated second order channel statistics. As a result, our proposed algorithm is highly robust to variations of the channel time-dynamics compared with other proposed approaches based on the estimation of the channel coefficients, rather than of their second-order statistics. In the proposed scheme, the BS probes the channel in the Downlink (DL) letting each user to estimate its own path direction. All the users within the BS coverage are trained simultaneously, without requiring "beam refinement" with multiple interactive rounds of Downlink/Uplink (DL/UL) transmissions, as done in other schemes. Thus, the training overhead of the proposed BA scheme is independent of the number of users in the system. We pose the channel estimation at the user side as a Compressed Sensing (CS) of a non-negative signal and use the recently developed Non-Negative Least Squares(NNLS) technique to solve it efficiently. The performance of our proposed algorithm is assessed via computer simulation in a relevant mm-Wave scenario. The results illustrate that our approach is superior to the state-of-the-art BA schemes proposed in the literature in terms of training overhead in multi-user scenarios and robustness to variations in the channel dynamics.
Geometric Control for Autonomous Landing on Asteroid Itokawa using Visual Localization
Venue: arXiv cs.SY 2017
Year: 2017

This paper considers the coupled orbit and attitude dynamics of a dumbbell spacecraft around an asteroid. Geometric methods are used to derive the coupled equations of motion, defined on the configuration space of the special Euclidean group, and then a nonlinear controller is designed to enable trajectory tracking of desired landing trajectories. Rather than relying on sliding mode control or optimization based methods, the proposed approach avoids the increased control utilization and computational complexity inherent in other techniques. The nonlinear controller is used to track a desired landing trajectory to the asteroid surface. A monocular imaging sensor is used to provide position and attitude estimates using visual odometry to enable relative state estimates. We demonstrate this control scheme with a landing simulation about asteroid Itokawa.
Inference of Fine-Grained Event Causality from Blogs and Films
Venue: arXiv cs.CL 2017
Year: 2017

Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80% of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire.
A Type II Fuzzy Entropy Based Multi-Level Image Thresholding Using Adaptive Plant Propagation Algorithm
Venue: arXiv cs.CV 2017
Year: 2017

One of the most straightforward, direct and efficient approaches to Image Segmentation is Image Thresholding. Multi-level Image Thresholding is an essential viewpoint in many image processing and Pattern Recognition based real-time applications which can effectively and efficiently classify the pixels into various groups denoting multiple regions in an Image. Thresholding based Image Segmentation using fuzzy entropy combined with intelligent optimization approaches are commonly used direct methods to properly identify the thresholds so that they can be used to segment an Image accurately. In this paper a novel approach for multi-level image thresholding is proposed using Type II Fuzzy sets combined with Adaptive Plant Propagation Algorithm (APPA). Obtaining the optimal thresholds for an image by maximizing the entropy is extremely tedious and time consuming with increase in the number of thresholds. Hence, Adaptive Plant Propagation Algorithm (APPA), a memetic algorithm based on plant intelligence, is used for fast and efficient selection of optimal thresholds. This fact is reasonably justified by comparing the accuracy of the outcomes and computational time consumed by other modern state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA) and Genetic Algorithm (GA).
Towards On-Chip Optical FFTs for Convolutional Neural Networks
Venue: arXiv cs.ET 2017
Year: 2017

Convolutional neural networks have become an essential element of spatial deep learning systems. In the prevailing architecture, the convolution operation is performed with Fast Fourier Transforms (FFT) electronically in GPUs. The parallelism of GPUs provides an efficiency over CPUs, however both approaches being electronic are bound by the speed and power limits of the interconnect delay inside the circuits. Here we present a silicon photonics based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently. Our all-optical FFT is based on nested Mach-Zender Interferometers, directional couplers, and phase shifters, with backend electro-optic modulators for sampling. The FFT delay depends only on the propagation delay of the optical signal through the silicon photonics structures. Designing and analyzing the performance of a convolutional neural network deployed with our on-chip optical FFT, we find dramatic improvements by up to 10^4 when compared to state-of-the-art GPUs when exploring a compounded figure-of-merit given by power per convolution over area. At a high level, this performance is enabled by mapping the desired mathematical function, an FFT, synergistically onto hardware, in this case optical delay interferometers.
A secure blind watermarking scheme based on DCT domain of the scrambled image
Venue: arXiv cs.CR 2017
Year: 2017

This paper investigates a secure blind watermarking scheme. The main idea of the scheme not only protects the watermark information but also the embedding positions. To achieve a higher level of security, we propose a sub key generation mechanism based on the singular value decomposition and hash function, where sub keys depend on both the main key and the feature codes of the original image. The different sub keys ensure that the embedding positions are randomly selected for different original images. Watermark is embedded in the Discrete Cosine Transform (DCT) coefficients of the scrambled original image. Simulation results show that such embedded method resolves well the contradiction of imperceptibility and robustness. Based on good correlation properties of chaotic sequences, we design a detection method, which can accurately compute geometric transformation (rotation and translation transformations) parameters. The security analysis, including key space analysis, key sensitivity analysis, cryptanalysis, and the comparison results demonstrate that the proposed watermarking scheme also achieves high security.
A Novel Scheduling Framework Leveraging Hardware Cache Partitioning for Cache-Side-Channel Elimination in Clouds
Venue: arXiv cs.CR 2017
Year: 2017

While there exist many isolation mechanisms that are available to cloud service providers, including virtual machines, containers, etc., the problem of side-channel increases in importance as a remaining security vulnerability, particularly in the presence of shared caches and multicore processors. In this paper we present a hardware-software mechanism that improves the isolation of cloud processes in the presence of shared caches on multicore chips. Combining the Intel CAT architecture that enables cache partitioning on the fly with novel scheduling techniques and state cleansing mechanisms, we enable cache-side-channel free computing for Linux-based containers and virtual machines, in particular, those managed by KVM. We do a preliminary evaluation of our system using a CPU bound workload. Our system allows Simultaneous Multithreading (SMT) to remain enabled and does not require application level changes.
Advanced Datapath Synthesis using Graph Isomorphism
Venue: arXiv cs.AR 2017
Year: 2017

This paper presents an advanced DAG-based algorithm for datapath synthesis that targets area minimization using logic-level resource sharing. The problem of identifying common specification logic is formulated using unweighted graph isomorphism problem, in contrast to a weighted graph isomorphism using AIGs. In the context of gate-level datapath circuits, our algorithm solves the un- weighted graph isomorphism problem in linear time. The experiments are conducted within an industrial synthesis flow that includes the complete high-level synthesis, logic synthesis and placement and route procedures. Experimental results show a significant runtime improvements compared to the existing datapath synthesis algorithms.
Context-aware Cluster Based Device-to-Device Communication to Serve Machine Type Communications
Venue: arXiv cs.NI 2017
Year: 2017

Billions of Machine Type Communication (MTC) devices are foreseen to be deployed in next ten years and therefore potentially open a new market for next generation wireless network. However, MTC applications have different characteristics and requirements compared with the services provided by legacy cellular networks. For instance, an MTC device sporadically requires to transmit a small data packet containing information generated by sensors. At the same time, due to the massive deployment of MTC devices, it is inefficient to charge their batteries manually and thus a long battery life is required for MTC devices. In this sense, legacy networks designed to serve human-driven traffics in real time can not support MTC efficiently. In order to improve the availability and battery life of MTC devices, context-aware device-to-device (D2D) communication is exploited in this paper. By applying D2D communication, some MTC users can serve as relays for other MTC users who experience bad channel conditions. Moreover, signaling schemes are also designed to enable the collection of context information and support the proposed D2D communication scheme. Last but not least, a system level simulator is implemented to evaluate the system performance of the proposed technologies and a large performance gain is shown by the numerical results.
An Incremental Redundancy HARQ Scheme for Polar Code
Venue: arXiv cs.IT 2017
Year: 2017

A polar code extension method which supports incremental redundancy hybrid ARQ(IR-HARQ) is proposed in this paper. By copying information bits to proper positions of the extend part, the extended polar code can give additional protection for the bits weakly protected by the first transmission. A comparison between the proposed algorithm with directly generated polar code, LTE turbo code, and some other IR-HARQ supporting polar code is given. Simulation results show that the proposed algorithm has nearly the same performance as directly generated polar code, which is chosen as base line for comparison.
Seminar Innovation Management - Winter Term 2017
Venue: arXiv cs.HC 2017
Year: 2017

This document contains the results obtained by the Innovation Management Seminar in winter term 2017. In total 11 ideas have been developed by the team. In the document all 11 ideas show improvements for future applications in ophthalmology. The 11 ideas are AR/VR Glasses with Medical Applications, Augmented Reality Eye Surgery, Game Diagnosis, Intelligent Adapting Glasses, MD Facebook, Medical Crowd Segmentation, Personalized 3D Model of the Human Eye, Photoacoustic Contact Lens, Power Supply Smart Contact Lens, VR-Cornea and Head Mount for Fundus Imaging
Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning
Venue: arXiv cs.CR 2017
Year: 2017

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.
Programmable DNA-mediated decision maker
Venue: arXiv cs.ET 2017
Year: 2017

DNA-mediated computing is a novel technology that seeks to capitalize on the enormous informational capacity of DNA and has tremendous computational ability to compete with the current silicon-mediated computing, due to massive parallelism and unique characteristics inherent in DNA interaction. In this paper, the methodology of DNA-mediated computing is utilized to enrich decision theory, by demonstrating how a novel programmable DNA-mediated normative decision-making apparatus is able to capture rational choice under uncertainty.